The Scanner AI found something on model-unfetter that needs your input before I can proceed.
In unfetter/models/llama.py, properties such as rope_theta, intermediate_size, and num_key_value_heads provide fallback default values (e.g., rope_theta = 10000.0). While these are currently used in get_summary(), I need to verify if the core ablation engine (core/ablation.py) uses these properties to perform mathematical operations on the weights. If they are used for logic, the Llama 2 defaults will cause incorrect projections for Llama 3/3.1/3.2 models, which use significantly different constants. Please clarify if these properties are purely for reporting or if they influence the orthogonalization math.
Please reply with your decision and I'll pick it up on the next cycle.
Generated by Triple-AI Pipeline (Scanner flagged OPEN_ISSUE)
The Scanner AI found something on model-unfetter that needs your input before I can proceed.
In unfetter/models/llama.py, properties such as rope_theta, intermediate_size, and num_key_value_heads provide fallback default values (e.g., rope_theta = 10000.0). While these are currently used in get_summary(), I need to verify if the core ablation engine (core/ablation.py) uses these properties to perform mathematical operations on the weights. If they are used for logic, the Llama 2 defaults will cause incorrect projections for Llama 3/3.1/3.2 models, which use significantly different constants. Please clarify if these properties are purely for reporting or if they influence the orthogonalization math.
Please reply with your decision and I'll pick it up on the next cycle.
Generated by Triple-AI Pipeline (Scanner flagged OPEN_ISSUE)